//! MRE (Magnetic Resonance Elastography) Inverse Solver
//!
//! This crate implements a Physics-Informed Neural Network (PINN) for solving
//! the inverse Helmholtz equation to recover tissue stiffness maps from
//! MRI-measured wave fields.
//!
//! # Physics Background
//!
//! MRE measures the propagation of mechanical waves through tissue.
//! The governing equation is the Helmholtz equation:
//!
//! ```text
//! div(mu * grad(u)) + rho * omega^2 * u = 0
//! ```
//!
//! where:
//! - `u(x,y)` is the complex wave displacement field (measured)
//! - `mu(x,y)` is the shear modulus (stiffness) - what we want to find
//! - `rho` is tissue density (~1000 kg/m^3)
//! - `omega` is angular frequency (2*pi*f)
//!
//! # Architecture
//!
//! The solver uses a hybrid approach:
//! 1. **Wave Net**: Neural network approximating u(x,y) with analytical derivatives
//! 2. **Stiffness Texture**: Learnable 2D grid for mu(x,y)
//!
//! Key innovation: Analytical 2nd derivatives through the Wave Net using
//! chain rule (not autograd or finite differences) for maximum accuracy.

pub mod config;
pub mod helmholtz;
pub mod phantom;
pub mod solver;
pub mod stiffness_texture;
pub mod training;
pub mod wave_net;

// Re-exports
pub use config::MreConfig;
pub use helmholtz::HelmholtzResidual;
pub use phantom::PhantomGenerator;
pub use solver::MreSolver;
pub use stiffness_texture::StiffnessTexture;
pub use wave_net::{WaveDerivatives, WaveNet};

// Re-export shared types for convenience
pub use mre_shared::{
    LossRecord, MreSnapshot, MreStatus, PhantomConfig, Point2D, StiffnessField, TissueProperties,
    TissueRegion, WaveField,
};